Teste-t pareado no software gratuito Jamovi

In this video, I explain how to perform a paired t-test (for two dependent samples) using Jamovi. I also teach how to test the assumptions of the paired t-test: the normality of the differences (using the Shapiro-Wilk test). In addition, I explain how to report the results found. Database download: https://drive.google.com/file/d/1sFeQ... Want to hire me for consulting or to analyze your data? The link below explains these services and provides a form for you to contact me: https://fernandafperes.com.br/servicos BIBLIOGRAPHIC REFERENCES: Article (in Portuguese) comparing normality tests (and concluding that Shapiro-Wilk is superior to the others, even in smaller samples), and suggesting evaluating the normality of the dependent variable by group: Leotti, V. B., Coster, R., & Riboldi, J. (2012). Normality of variables: verification methods and comparison of some non-parametric tests by simulation. Revista HCPA. Porto Alegre. Vol. 32, no. 2 (2012), p. 227-234. Article (in English) comparing normality tests, and concluding that Shapiro-Wilk has the greatest power: Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests. Journal of statistical modeling and analytics, 2(1), 21-33. Article (in Portuguese) discussing alternatives to situations where data do not have a normal distribution: Paes, A. T. (2009). What to do when the distribution is not normal. Einstein–Continuing Education in Health, 7, 1. Article (in English) discussing that parametric tests are reasonably robust to violations of normality when n is large: Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual review of public health, 23(1), 151-169. Berben, L., Sereika, S. M., & Engberg, S. (2012). Effect size estimation: methods and examples. International journal of nursing studies, 49(8), 1039-1047. Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in psychology, 4, 863. Cumming, G. (2013). Cohen’s d needs to be readily interpretable: Comment on Shieh (2013). Behavior Research Methods, 45(4), 968-971. Savilowsky, S. S. (2009). New effect size rules of thumb. Journal of Modern Applied Statistical Methods, 8(2), 26. Espírito Santo, H., & Daniel, F. (2017). Calculating and presenting effect sizes in scientific papers (1): the limitations of p 0.05 in the analysis of differences in means of two groups. Portuguese Journal of Behavioral and Social Research, 1(1), 3-16. Follow the statistics content also on Instagram:   / estatisticaaplicada